Aspects of the disclosure relate to computing technologies, including computer software and computer hardware. In particular, various aspects of the disclosure relate to using small cells (e.g., micro, pico or femto cells) as reliable crowdsourcing agents.
A number of software applications, websites, and other functionalities that can be provided on a mobile device are beginning to use position information to further enhance a user's experience when using such a device. For example, a device may access a particular website, such as a restaurant review website, and the website may use information about the device's current location in order to provide location-specific content, such as reviews of nearby restaurants, to the device. Other applications and websites may, for instance, use information about a device's current location to display relevant maps to a user, provide the user with information about local businesses, or inform the user of a local weather forecast.
An ability to estimate a mobile device's location may be made possible by any one of several signals-based position estimation technologies such as, for example, satellite positioning systems (e.g., the Global Positioning System (GPS) and the like), advanced forward-link trilateration (AFLT), observed time difference of arrival (OTDOA), enhanced cellular identification (ECID), just to name a few examples.
In many instances, a mobile device that estimates a position as a result of receiving signals from base stations of a cellular network or from space vehicles of a satellite positioning system (SPS), for example, may be assisted by signals from a terrestrial cellular voice or data communications system. Such assistance may reduce a time required by the mobile device to acquire positioning signals, and may include information to allow position calculation, such as location of base stations or access points, timing of or between base stations, positioning reference signal (PRS) structure information, and the like.
One way for obtaining such assistance information (e.g., base station's location, timing between base stations, radio parameters) is via mobile device crowdsourcing.
In mobile device crowdsourcing, a multitude of mobile devices send observed data to a crowdsourcing server. Examples of observed data can include signal strength information, timing information of base stations or between base stations, round-trip-time (RTT) measurements, or the like. The observed data can further be associated with a particular source identifier (e.g., a cell-identifier (ID) of the base station, medium access control (MAC) address of the access point) and tagged with the mobile device's location, if available. A crowdsourcing server can estimate information based on the received observed data from multiple mobile devices. The estimated information (e.g., base station/access point locations, base station/access point coverage areas, base station/access point timing) can be stored in a database. The database may be used for assisted position calculation, or to provide assistance data to other mobile devices in the network. Additionally, even though an individual mobile device-observed data may be inaccurate, the consensus of the multitude of mobile devices can be more precise. Furthermore, mobile device crowdsourcing may obtain necessary information of the radio network (e.g., base station/access point locations, timing information) which would otherwise be difficult or impossible to obtain.
Crowdsourcing via mobile devices may encounter several disadvantages associated with using a mobile device as a crowdsourcing agent including limited battery life, position uncertainty, and comparative availability. The crowdsourcing activity in the mobile device may impact user experience (e.g., significantly draining the battery, make the mobile device less responsive to user interactions). Additionally, it can require bandwidth of the communications network for uploading the measurement data to the server over the air. In addition, the mobile device's location may be needed for the server to correctly estimate information based on the observed data. The mobile device's location may not be available or obtaining it may further drain the battery. The quality of the information maintained by the crowdsourcing servers can be inaccurate if the mobile device's location is inaccurate.
Furthermore, there may be privacy concerns with the mobile device crowdsourcing approach, since the mobile device is associated with a particular user/subscriber, and the observed data may reveal information about the user (e.g., his/her location). Therefore, the user of the mobile device can have the option to explicitly allow or deny the reporting of the observed data. If many users deny the data reporting, the quality of the information maintained by the crowdsourcing servers might be less than desired.